Our MM-RealSR adopts the complex and high-order degradation synthesis [1]}, [2]} to simulate the real-world degradations.
In such a way, we can generate several degraded images with different degradation strengths onto one sample,
which is important to construct pairs for our proposed method with metric learning. Our experiments have shown its effectiveness in learning the degradation estimation and modulating performance in real-world samples. However, it is still hard to directly apply our method to train on real samples.
The challenge is that real samples usually do not contain a pair of images with the same content but with different degradations.
Our work makes the first attempt towards interactive modulation in RWSR, and there is still a long way with challenges, which deserves further research.
